This invention is directed to object recognition and counting and, more particularly, to recognizing and counting geometrically distinct located objects in a field of objects of varying types.
While this invention was developed for use in detecting and counting particular types of biological cells located in a field of cells of varying types, specifically reticulated red blood cells in a blood sample, and is primarily described in such an environment, it will be appreciated by those skilled in the art and others from the following description that the invention is also useful in other environments. Generally, the invention is useful in any environment where it is desired to recognize and count the number of geometrically similar objects, located in a mass of objects having various geometrical shapes, where the mass of objects can be arrayed in a non-overlapping monolayer. The invention is particularly useful where the objects are small, e.g., cellular in size, but range in equivalent size from 0.01 .mu. to 100 .mu. for common objects. It should be recognized that larger objects can be imaged to this equivalent size.
For nearly the last two decades biomedical research has focused on automating the acquisition and interpretation of data in cytology and microscopy. A variety of techniques has been proposed and, in some cases, implemented. In many such proposals the optical characteristics of the objects (e.g., cells) have formed the basis of the proposed technique. For example, techniques depending on the optical absorption, fluorescence and scattering properties of cells have been proposed and in some cases used to separate and classify cells. One difficulty with using cellular optical properties to distinguish between cell types is that, since a single cell is examined at a time, chemical reactions are often needed to create or enhance the optical characteristics upon which these techniques depend. As a result, these techniques rely heavily on the ability to create the needed chemical reactions, rather than solely on optical properties. Thus, although continuous flow systems based on optical information can analyze up to several thousand cells per second with a high degree of repeatability, because cell classification depends on external factors (e.g., chemical reactions), the possibility of error is higher and more empirical. Moreover, many of these systems are very dependent on the use of electronic digital computers to perform a variety of time-consuming, and therefore expensive, processing steps. This expense has made such systems particularly undesirable for use in general clinical environments.
As a result of the foregoing difficulties, consideration has been given to the use of optical data processing techniques (as opposed to electronic data processing techniques) to identify and count objects. Optical data processing techniques are based on the knowledge that geometrically distinct objects will scatter light in a distinct manner, and that each geometrically similar object will scatter light in a similar manner.
As will be recognized by those skilled in the data processing art, optical data processing techniques function in an analog, as opposed to a digital, manner. Further, because of the parallel nature of the data processing analog operations can be performed at substantially higher processing rates and with higher data capacity, than can digital operations, particularly in the area of summing data for subsequent analysis.
One of the difficulties in applying optical data processing techniques, as well as other techniques, to the recognition and counting of objects, such as biological cells, is the inherent requirement that the resultant system exactly and unambiguously identify and count the particular cell population desired.
In the past, attempts to meet the foregoing constraint have involved applying matched filter concepts to provide a system wherein only the light scattered by the objects to be recognized and counted is passed. The problem with the use of matched filters is that they suffer from rotational alignment dependencies and, require an unambiguous description of the Weiner spectrum of the cell or object to be recognized and counted.
In many environments the need for an unambiguous or exact count can be met using an estimated count, depending upon the required degree of "exactness." An estimation approach is particularly attractive when the resultant count is to be used for threshold or screening purposes. But, estimation optical data processing systems also have the problem that they require the Weiner spectrum of the desired cell be identified. However, in an estimation system this requirement can be dealt with by using a statistical approach. Specifically, instead of attempting to isolate a single cell for use in identifying or determining the Weiner spectrum of the cells to be counted, an ensemble of cells can be used to form an average spectrum. In this regard, attention is directed to U.S. Pat. No. 3,947,123, issued Mar. 30, 1976 to F. Paul Carlson, et al., for "Coherent Optical Analyzer."
While the optical data processing method and apparatus described in the foregoing patent is an advance over prior methods and apparatus and lends itself to studies of cell types, groups, or subclasses, by simply varying the ensemble selected to develop the average spectrum, it has certain disadvantages. For example, the system is limited by its need to continually fabricate a new Weiner filter for each new class or group to be examined. Further, the method and apparatus implicitly requires that an ensemble of the particular cell to be identified and counted be unambiguously isolated. Obviously, this inflexible filter fabrication requirement significantly limits the extension of this method and apparatus to other environments.
Another previous problem with applying optical data processing tehniques to cell recognition and counting is that of interfacing a coherent optical system to the cells to be counted, both in an input and output sense. In the case of cells on a film slide, the input problem can be readily resolved by creating a monolayer of the cells. However, the output problem remains unless indirect measurements, such as integrating the total output, is acceptable. In many cases, such an indirect measurement is unacceptable or, at best, is less acceptable than desired.
Therefore, it is an object of this invention to provide a new and improved optical data processing method and apparatus for identifying and counting objects.
It is a further object of this invention to provide a new and improved optical data processing method and apparatus for recognizing and counting the number of objects of a particular type in a field of objects of varying types that is based on the geometrical distinctness of the objects.
It is a still further object of this invention to provide an uncomplicated optical data processing method and apparatus suitable for recognizing and counting the number of biological cells of a particular morphological type in a field of biological cells of varying types.
It is also an object of this invention to provide a method of determining the vector positions at which measurements are to be made in a pattern recognition system.